Variational Bayesian grammar induction for natural language

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4201 LNAI, Issue , 2006, Pages 84-96

  Kurihara, Kenichi ^a   Sato, Taisuke ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; FREE ENERGY; LEARNING SYSTEMS; PROBABILITY;

GRAMMATICAL STRUCTURES; MODEL SELECTION; PCFG INDUCTION ALGORITHMS; PROBABILISTIC CONTEXT-FREE GRAMMARS (PCFG);

CONTEXT FREE GRAMMARS;

EID: 33750292464     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11872436_8     Document Type: Conference Paper

References (19)

1. Hagai Attias. A variational Bayesian framework for graphical models. In Advances in Neural Information Processing Systems, volume 12, 2000.
2. J. K. Baker. Trainable grammars for speech recognition. In D. H. Klatt and J. J. Wolf, editors, Speech Communication Papers for the 97th Meeting of the Acoustical Society of America, pages 547-550, 1979.
3. Joseph Bockhorst and Mark Craven. Refining the structure of a stochastic context-free grammar. In Proceedings of the 17th International Joint Conference on Artificial Intelligence, 2001.
4. Stanley F. Chen. Bayesian grammar induction for language modeling. In Meeting of the Association for Computational Linguistics, pages 228-235, 1995.
5. Michael Collins. Discriminative reranking for natural language parsing. In Proc. 17th International Conf. on Machine Learning, pages 175-182, 2000.
6. Zoubin Ghahramani and Matthew J. Beal. Variational inference for Bayesian mixtures of factor analysers. In Advances in Neural Information Processing Systems, volume 12, 2000.
7. Wide R. Hogenhout and Yuji Matsumoto. A fast method for statistical grammar induction. Natural Language Engineering, 4(3): 191-209, 1998.
8. Dan Klein and Christopher D. Manning. A generative constituent-context model for improved grammar induction. In Proceedings of the 40th Annual Meeting of the ACL, 2002.
9. Dan Klein and Christopher D. Manning. Corpus-based induction of syntactic structure: Models of dependency and constituency. In Proceedings of the 42nd Annual Meeting of the ACL, 2004.
10. Kenichi Kurihara and Taisuke Sato. An application of the variational Bayesian approach to probabilistic context-free grammars, 2004. IJCNLP-04 Workshop beyond shallow analyses.
11. Karim Lari and Steve Young. The estimation of stochastic context-free grammars using the inside-outside algorithm. Computer Speech and Language, 4:35-56, 1990.
12. David J.C. MacKay. em ensemble learning for hidden markov models. Technical report, 1997.
13. Fernando C. N. Pereira and Yves Schabes. Inside-outside reestimation from partially bracketed corpora. In Meeting of the Association for Computational Linguistics, pages 128-135, 1992.
14. Masaaki Sato. Online model selection based on the variational bayes. In Neural Computation, volume 13, pages 1649-1681, 2001.
15. Yves Schabes, Michal Roth, and Randy Osborne. Parsing the wall street journal with the inside-outside algorithm. In ACL, pages 341-347, 1993.
16. Andreas Stolcke and Stephen Omohundro. Inducing probabilistic grammars by Bayesian model merging. In International Conference on Grammatical Inference, 1994.
17. Naonori Ueda and Zoubin Ghahramani. Bayesian model search for mixture models based on optimizing variational bounds. Neural Networks, 15(10):1223-1241, 2002.
18. Menno van Zaanen. Abl: Alighment-based learning. In COLING, volume 18, pages 961-967, 2000.
19. Kiri Wagsta, Claire Cardie, Seth Rogers, and Stefan Schroedl. Constrained k-means clustering with background knowledge. In Proceedings of 18th International Conference on Machine Learning, pages 577-584, 2001.